Instructions to use francipam/evospazio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use francipam/evospazio with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("francipam/evospazio", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- cdb81e7a6927e83dcedff5e941c887566153c6c0a21cbe3985264fd6c344795e
- Size of remote file:
- 1.01 MB
- SHA256:
- 15e775cc15dc9c195ba2589d906c0b16601ca7ebefca73a1400b8e659b72e167
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